As new innovative and increasingly sophisticated image processing techniques are continually reported in the medical imaging literature, concurrent sophistication in methods for critical evaluation and quality control is lacking. Despit numerous reports of novel DIR algorithms and their potential diagnostic and therapeutic medical applications, the scientific literature is lacking standardized procedures for DIR performance evaluation, comparison testing, and validation specific to medical application. Expert-determined anatomic feature-pairs have the potential to become a widely adopted reference for evaluating DIR spatial accuracy; however, there is still great variability in their use. Statistical methods fr analyzing the matched landmark pairs have been limited to descriptive statistics summarizing the measured registration errors, failing to account for uncertainty in anatomic localization, variability among observers, and voxel discretization of the image space. The utility of Bayesian methods in the interpretation of modern medical research data has long been recognized. For our purposes, the strength of a Bayesian approach is one that allows judgment regarding an algorithm's performance characteristics to be derived from multiple sources, including multiple observers for feature-pair localization, multiple imaging modalities, and independent reference datasets. This facilitates interpretation of the measured data, and allows us to incorporate knowledge of the imaging acquisition and reconstruction process into formulation of prior distributions reflective of the underlying physical processes. This results in a more complete representation of an algorithm's spatial accuracy performance than is available today. The goal of the proposed research is to develop a computational framework and software infrastructure for Bayesian analysis of deformable image registration spatial accuracy. Software for performing these analyses will be incorporated into a publicly available reference image database, allowing investigators to quantitatively evaluate and compare multiple image registration algorithms/implementations on a common dataset, within a standard analysis framework that is currently lacking. The Specific Aims of the proposed research are: 1. Create a reference library of cases to measure DIR spatial accuracy performance and uncertainty for inter-modality (CT-MRI) registration. 2. Develop and validate a Bayesian hierarchical model for DIR spatial accuracy evaluation using the expert selected landmark feature approach. 3. Disseminate software for standardized Bayesian analysis of DIR spatial accuracy. The availability of a common dataset for DIR evaluation that is broadly applicable will facilitate streamlined comparative evaluation and meta-analysis of the scientific literature, and provide a foundation upon which to develop a standardized evaluation methodology that is presently lacking. Additionally, there is much interest to adopt a multi-modality approach to pre-treatment radiotherapy (RT) planning and image guided RT delivery, in which the superior acquisition and soft-tissue characteristics of magnetic resonance imaging (MRI) are integrated with the electron density information and geometric fidelity inherent to computed tomography (CT). Inclusion of CT-MRI reference data will allow investigators to explore feasibility of a multi-modal approach to RT planning and image-guided delivery, which requires accurate spatial registration of the complementary datasets. By providing a rigorous computational framework for incorporating uncertainty in the use of anatomic feature-pairs for DIR evaluation, the proposed study has the potential to shape future protocol guidelines for clinical validation, acceptance testing, and quality assurance of DIR in medical imaging.